--------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/ignaciojurado/Dropbox/My Mac (MacBook-Pro.home)/Downloads/dataverse_files (1)/
> Replication.log
  log type:  text
 opened on:  10 Jul 2023, 18:07:55

. ************************+
. ***************************
. **ANALYSES
. ***************************
. ****************
. 
. use "Data_Officers_replication.dta", clear

. 
. 
. 
. *******************+
. * MAIN REGRESSIONS
. ***********************
. 
. 
. *1 MODELS WITHOUT COVARIATES
. 
. logit  vote_2016 treatment, 

Iteration 0:   log likelihood = -736.71793  
Iteration 1:   log likelihood = -736.27162  
Iteration 2:   log likelihood = -736.27054  
Iteration 3:   log likelihood = -736.27054  

Logistic regression                                     Number of obs =  1,792
                                                        LR chi2(1)    =   0.89
                                                        Prob > chi2   = 0.3442
Log likelihood = -736.27054                             Pseudo R2     = 0.0006

------------------------------------------------------------------------------
   vote_2016 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .1813252   .1948047     0.93   0.352    -.2004851    .5631354
       _cons |   1.760495   .0726574    24.23   0.000     1.618089    1.902901
------------------------------------------------------------------------------

. margins, dydx(   treatment) post

Average marginal effects                                 Number of obs = 1,792
Model VCE: OIM

Expression: Pr(vote_2016), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0222645   .0239214     0.93   0.352    -.0246207    .0691497
------------------------------------------------------------------------------

. estimates store s1

. 
. 
. logit  vote_april  treatment, 

Iteration 0:   log likelihood = -549.99983  
Iteration 1:   log likelihood = -540.01462  
Iteration 2:   log likelihood = -539.64607  
Iteration 3:   log likelihood = -539.64577  
Iteration 4:   log likelihood = -539.64577  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(1)    =  20.71
                                                        Prob > chi2   = 0.0000
Log likelihood = -539.64577                             Pseudo R2     = 0.0188

------------------------------------------------------------------------------
  vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   1.234121    .318811     3.87   0.000     .6092628    1.858979
       _cons |   1.958971   .0836661    23.41   0.000     1.794988    2.122953
------------------------------------------------------------------------------

. margins, dydx( treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_april), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .1184872   .0310738     3.81   0.000     .0575836    .1793908
------------------------------------------------------------------------------

. estimates store s2

. 
. 
. logit   vote_may  treatment, 

Iteration 0:   log likelihood = -747.73911  
Iteration 1:   log likelihood = -743.32777  
Iteration 2:   log likelihood =   -743.305  
Iteration 3:   log likelihood =   -743.305  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(1)    =   8.87
                                                        Prob > chi2   = 0.0029
Log likelihood = -743.305                               Pseudo R2     = 0.0059

------------------------------------------------------------------------------
    vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .5607339   .1982132     2.83   0.005     .1722432    .9492246
       _cons |    1.44809   .0701455    20.64   0.000     1.310607    1.585573
------------------------------------------------------------------------------

. margins, dydx( treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0815249   .0287659     2.83   0.005     .0251447     .137905
------------------------------------------------------------------------------

. estimates store s3

. 
. 
. 
. logit   vote_nov treatment, 

Iteration 0:   log likelihood = -571.91926  
Iteration 1:   log likelihood = -571.85887  
Iteration 2:   log likelihood = -571.85885  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(1)    =   0.12
                                                        Prob > chi2   = 0.7282
Log likelihood = -571.85885                             Pseudo R2     = 0.0001

-------------------------------------------------------------------------------
vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    treatment |   .0696596   .2014754     0.35   0.730    -.3252248    .4645441
        _cons |   1.647991   .0830222    19.85   0.000     1.485271    1.810712
-------------------------------------------------------------------------------

. margins, dydx(   treatment) post

Average marginal effects                                 Number of obs = 1,302
Model VCE: OIM

Expression: Pr(vote_november), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0093497   .0270415     0.35   0.730    -.0436506    .0623501
------------------------------------------------------------------------------

. estimates store s4

. 
. coefplot (s1 , label( "Turnout 2016 (Placebo)")) ( s2, label( "Turnout April 2019")) ///
>  ( s3, label( "Turnout May 2019 (Short-term effect)")) ( s4, label("Turnout November 2019 (Long-
> term effect)")) ///
>  , keep ( treatment) vertical yline(0) ylabel(-0.04(.04).2)  xlabel("") title ("Models without c
> ovariates") legend(position(6))  ytitle("ATE")  level(95)

. 
.  graph copy g1

.  
. *2 MODELS WITH COVARIATES
. 
. logit  vote_2016  treatment female age low_education high_educat i.employmentstatus 

Iteration 0:   log likelihood = -736.71793  
Iteration 1:   log likelihood = -724.00487  
Iteration 2:   log likelihood = -723.59261  
Iteration 3:   log likelihood = -723.59075  
Iteration 4:   log likelihood = -723.59075  

Logistic regression                                     Number of obs =  1,792
                                                        LR chi2(9)    =  26.25
                                                        Prob > chi2   = 0.0019
Log likelihood = -723.59075                             Pseudo R2     = 0.0178

----------------------------------------------------------------------------------
       vote_2016 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .2444603   .1982222     1.23   0.217    -.1440481    .6329686
          female |   .1699624   .1446739     1.17   0.240    -.1135932     .453518
             age |   .0166896   .0068194     2.45   0.014     .0033238    .0300554
   low_education |  -.5035619   .2252953    -2.24   0.025    -.9451326   -.0619913
  high_education |   -.166789   .1501156    -1.11   0.267    -.4610101    .1274321
                 |
employmentstatus |
        Retired  |   .5904522   .3904844     1.51   0.131    -.1748833    1.355788
     Unemployed  |  -.2469715   .1928545    -1.28   0.200    -.6249595    .1310165
        Student  |   .4023675   .2601935     1.55   0.122    -.1076024    .9123373
    Housechores  |  -.3304033   .4158192    -0.79   0.427    -1.145394    .4845873
                 |
           _cons |   1.086706   .3051169     3.56   0.000     .4886881    1.684724
----------------------------------------------------------------------------------

. 
. margins, dydx(   treatment) post

Average marginal effects                                 Number of obs = 1,792
Model VCE: OIM

Expression: Pr(vote_2016), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0295913   .0239934     1.23   0.217    -.0174348    .0766174
------------------------------------------------------------------------------

. estimates store s1

. 
. logit  vote_april  treatment female age low_education high_educat i.employmentstatus 

Iteration 0:   log likelihood = -549.99983  
Iteration 1:   log likelihood = -528.14285  
Iteration 2:   log likelihood = -526.48355  
Iteration 3:   log likelihood =  -526.4753  
Iteration 4:   log likelihood =  -526.4753  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(9)    =  47.05
                                                        Prob > chi2   = 0.0000
Log likelihood = -526.4753                              Pseudo R2     = 0.0428

----------------------------------------------------------------------------------
      vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   1.254747   .3225147     3.89   0.000     .6226296    1.886864
          female |   .0400179   .1735128     0.23   0.818    -.3000609    .3800967
             age |   .0357098   .0083902     4.26   0.000     .0192654    .0521542
   low_education |  -.5034711   .2635073    -1.91   0.056    -1.019936    .0129939
  high_education |   .1977487   .1859846     1.06   0.288    -.1667744    .5622718
                 |
employmentstatus |
        Retired  |  -.4663483   .3503935    -1.33   0.183    -1.153107    .2204103
     Unemployed  |   -.239351   .2326141    -1.03   0.303    -.6952663    .2165644
        Student  |   .3684067   .2950318     1.25   0.212     -.209845    .9466584
    Housechores  |  -.3942728   .5142186    -0.77   0.443    -1.402123    .6135772
                 |
           _cons |   .5220833   .3645134     1.43   0.152    -.1923498    1.236516
----------------------------------------------------------------------------------

. margins, dydx(   treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_april), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .1182031   .0307636     3.84   0.000     .0579076    .1784987
------------------------------------------------------------------------------

. estimates store s2

. 
. 
. logit  vote_may  treatment female age low_education high_educat i.employmentstatus 

Iteration 0:   log likelihood = -747.73911  
Iteration 1:   log likelihood = -722.29121  
Iteration 2:   log likelihood = -721.54128  
Iteration 3:   log likelihood = -721.54024  
Iteration 4:   log likelihood = -721.54024  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(9)    =  52.40
                                                        Prob > chi2   = 0.0000
Log likelihood = -721.54024                             Pseudo R2     = 0.0350

----------------------------------------------------------------------------------
        vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .6150714   .2030021     3.03   0.002     .2171945    1.012948
          female |    .002777   .1420647     0.02   0.984    -.2756646    .2812186
             age |   .0343318   .0068791     4.99   0.000      .020849    .0478145
   low_education |  -.5623431   .2224401    -2.53   0.011    -.9983178   -.1263685
  high_education |   .2069719   .1508254     1.37   0.170    -.0886405    .5025843
                 |
employmentstatus |
        Retired  |    .047678   .3265628     0.15   0.884    -.5923732    .6877293
     Unemployed  |  -.1040245   .1969586    -0.53   0.597    -.4900562    .2820073
        Student  |   .2655284   .2398783     1.11   0.268    -.2046245    .7356812
    Housechores  |  -.8591972   .3767057    -2.28   0.023    -1.597527   -.1208677
                 |
           _cons |   .0455785   .3026591     0.15   0.880    -.5476225    .6387795
----------------------------------------------------------------------------------

. margins, dydx(   treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0868682   .0285714     3.04   0.002     .0308692    .1428671
------------------------------------------------------------------------------

. estimates store s3

. 
. 
. 
. logit   vote_nov  treatment female age low_education high_educat i.employmentstatus 

Iteration 0:   log likelihood = -571.91926  
Iteration 1:   log likelihood = -548.19902  
Iteration 2:   log likelihood = -546.54231  
Iteration 3:   log likelihood = -546.53664  
Iteration 4:   log likelihood = -546.53664  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(9)    =  50.77
                                                        Prob > chi2   = 0.0000
Log likelihood = -546.53664                             Pseudo R2     = 0.0444

----------------------------------------------------------------------------------
   vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .1035936   .2085536     0.50   0.619     -.305164    .5123512
          female |   .0586665   .1676649     0.35   0.726    -.2699507    .3872838
             age |    .025537   .0078597     3.25   0.001     .0101323    .0409417
   low_education |   -.805846   .2514392    -3.20   0.001    -1.298658   -.3130343
  high_education |   .2130898   .1778527     1.20   0.231     -.135495    .5616746
                 |
employmentstatus |
        Retired  |   .4749288   .4243574     1.12   0.263    -.3567965    1.306654
     Unemployed  |  -.4902077   .2128173    -2.30   0.021     -.907322   -.0730935
        Student  |  -.2528901   .2790604    -0.91   0.365    -.7998383    .2940582
    Housechores  |  -1.169129   .3947928    -2.96   0.003    -1.942908   -.3953491
                 |
           _cons |   .6997127   .3548165     1.97   0.049      .004285     1.39514
----------------------------------------------------------------------------------

. margins, dydx(   treatment) post

Average marginal effects                                 Number of obs = 1,302
Model VCE: OIM

Expression: Pr(vote_november), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0133203   .0268122     0.50   0.619    -.0392306    .0658711
------------------------------------------------------------------------------

. estimates store s4

. 
. 
. coefplot (s1 , label( "Turnout 2016 (Placebo)")) ( s2, label( "Turnout April 2019")) ///
>  ( s3, label( "Turnout May 2019 (Short-term effect)")) ( s4, label("Turnout November 2019 (Long-
> term effect)")) ///
>  , keep ( treatment) vertical yline(0) ylabel(-0.04(.04).2) xlabel("")  title ("Models with cova
> riates") legend(position(6))  ytitle("ATE") level(95)

. 
. 
.   graph copy g2

. 
.    graph combine g1 g2 ,  title("ATEs: Electoral Officers")

.   graph drop g1 g2

.  
.  
.  
.  
. ***************************
. *2 DIFF-IN-DIFF
. ***************************
. 
. 
. use "Data_Officers_replication.dta", clear

. 
. reg vote_2016 treatment

      Source |       SS           df       MS      Number of obs   =     1,792
-------------+----------------------------------   F(1, 1790)      =      0.87
       Model |  .106674757         1  .106674757   Prob > F        =    0.3517
    Residual |  220.035624     1,790   .12292493   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  220.142299     1,791  .122915857   Root MSE        =    .35061

------------------------------------------------------------------------------
   vote_2016 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0212803   .0228438     0.93   0.352    -.0235229    .0660835
       _cons |   .8532716   .0090136    94.66   0.000     .8355933      .87095
------------------------------------------------------------------------------

. 
. coefplot , vertical yline(0) keep(treatment) xlabel (1 "June 2016") ytitle ("Treatment Effect") 
> title ("Pre-Treatment Effect (2016)") ylabel (-.05(.025).125)

. 
. graph copy g1

. 
.  **As a Panel
.  gen vote_1=vote_2016

.  gen vote_2=vote_april
(292 missing values generated)

.  gen vote_3=vote_may
(292 missing values generated)

.  gen vote_4=vote_nov
(713 missing values generated)

.  
.  gen treatment_1=treatment
(789 missing values generated)

.  gen treatment_2=treatment
(789 missing values generated)

.  gen treatment_3=treatment
(789 missing values generated)

.  gen treatment_4=treatment
(789 missing values generated)

.  
.  gen region_1=region

.  gen region_2=region

.  gen region_3=region

.  gen region_4=region

.  
.  gen female_1=female

.  gen female_2=female

.  gen female_3=female

.  gen female_4=female

.  
.  gen education_1= education

.  gen education_2= education 

.  gen education_3= education 

.  gen education_4= education 

.  
.  gen  employmentstatus_1= employmentstatus

.  gen  employmentstatus_2= employmentstatus

.  gen  employmentstatus_3= employmentstatus

.  gen  employmentstatus_4= employmentstatus

. 
.  gen age_1= age

.  gen age_2= age

.  gen age_3= age

.  gen age_4= age

.  
.  gen highincome_1= income_high
(526 missing values generated)

.  gen highincome_2= income_high
(526 missing values generated)

.  gen highincome_3= income_high
(526 missing values generated)

.  gen highincome_4= income_high
(526 missing values generated)

.  
.  gen medincome_1= income_med
(526 missing values generated)

.  gen medincome_2= income_med
(526 missing values generated)

.  gen medincome_3= income_med
(526 missing values generated)

.  gen medincome_4= income_med
(526 missing values generated)

. 
.   
.  gen low_education_1=  low_education

.  gen low_education_2=  low_education 

.  gen low_education_3=  low_education 

.  gen low_education_4=  low_education 

.  
.   
.  gen high_education_1=  high_education

.  gen high_education_2=  high_education 

.  gen high_education_3=  high_education 

.  gen high_education_4=  high_education 

.  
.  
.  encode codpanelista, generate(panelista_num)

.  
.  
.  keep employmentstatus_* highincome_* medincome_*  education_* female_* age_* region_1 region_2 
> region_3 region_4 codpanelista panelista_num vote_1 vote_2 vote_3 vote_4 treatment_*  low_educat
> ion_* high_education_*

.  
.  reshape long vote_ treatment_ region_ employmentstatus_ education_ female_ age_  highincome_ me
> dincome_  high_education_ low_education_, i(codpanelista) j(wave)
(j = 1 2 3 4)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations            2,581   ->   10,324      
Number of variables                  46   ->   14          
j variable (4 values)                     ->   wave
xij variables:
               vote_1 vote_2 ... vote_4   ->   vote_
treatment_1 treatment_2 ... treatment_4   ->   treatment_
         region_1 region_2 ... region_4   ->   region_
employmentstatus_1 employmentstatus_2 ... employmentstatus_4->employmentstatus_
education_1 education_2 ... education_4   ->   education_
         female_1 female_2 ... female_4   ->   female_
                  age_1 age_2 ... age_4   ->   age_
highincome_1 highincome_2 ... highincome_4->   highincome_
medincome_1 medincome_2 ... medincome_4   ->   medincome_
high_education_1 high_education_2 ... high_education_4->high_education_
low_education_1 low_education_2 ... low_education_4->low_education_
-----------------------------------------------------------------------------

.    
. xtset panelista_num wave

Panel variable: panelista_num (strongly balanced)
 Time variable: wave, 1 to 4
         Delta: 1 unit

. 
. tab wave, gen(ww)

       wave |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      2,581       25.00       25.00
          2 |      2,581       25.00       50.00
          3 |      2,581       25.00       75.00
          4 |      2,581       25.00      100.00
------------+-----------------------------------
      Total |     10,324      100.00

. gen int2=treatment_*ww2
(3,156 missing values generated)

. gen int3=treatment_*ww3
(3,156 missing values generated)

. gen int4=treatment_*ww4
(3,156 missing values generated)

. 
. xtreg vote_ treatment_ i.wave int2 int3 int4 age_ female_ low_education_ high_education_   i.emp
> loymentstatus_ 

Random-effects GLS regression                   Number of obs     =      6,290
Group variable: panelista_~m                    Number of groups  =      1,792

R-squared:                                      Obs per group:
     Within  = 0.0144                                         min =          1
     Between = 0.0401                                         avg =        3.5
     Overall = 0.0284                                         max =          4

                                                Wald chi2(15)     =     139.67
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

-----------------------------------------------------------------------------------
            vote_ | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
       treatment_ |   .0253507   .0230235     1.10   0.271    -.0197746     .070476
                  |
             wave |
               2  |   .0212917   .0106069     2.01   0.045     .0005026    .0420807
               3  |  -.0454256   .0106069    -4.28   0.000    -.0662146   -.0246365
               4  |   -.024527   .0113565    -2.16   0.031    -.0467854   -.0022686
                  |
             int2 |   .0647298   .0258171     2.51   0.012     .0141293    .1153304
             int3 |    .052594   .0258171     2.04   0.042     .0019934    .1031946
             int4 |    .001687   .0274694     0.06   0.951    -.0521521    .0555261
             age_ |   .0035388   .0006331     5.59   0.000     .0022981    .0047796
          female_ |   .0074944   .0132441     0.57   0.571    -.0184635    .0334524
   low_education_ |  -.0810335   .0228327    -3.55   0.000    -.1257847   -.0362823
  high_education_ |   .0105611    .013778     0.77   0.443    -.0164432    .0375655
                  |
employmentstatus_ |
               2  |   .0068253   .0268487     0.25   0.799    -.0457972    .0594478
               3  |  -.0360771   .0192152    -1.88   0.060    -.0737383    .0015841
               4  |   .0348681    .024138     1.44   0.149    -.0124415    .0821777
               5  |  -.1030056    .041611    -2.48   0.013    -.1845617   -.0214495
                  |
            _cons |   .7095563   .0295774    23.99   0.000     .6515857    .7675269
------------------+----------------------------------------------------------------
          sigma_u |  .21409608
          sigma_e |  .27823416
              rho |  .37189954   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------

. coefplot, keep(int2 int3 int4) vertical yline(0)  xlabel(1 "April 2019" 2 "May 2019" 3 "November
>  2019") title ("Diff-in-Diff Estimation") ytitle ("Treatment Effect") ylabel (-.05(.025).125) le
> vel(95)

. graph copy g2

. graph combine g1 g2

. graph drop  g1 g2

. 
. 
. 
. *********************
. * 3. ATTITUDES
. *********************
. 
. use "Data_Officers_replication.dta" , clear

. 
. ***************************
. *Create change variables
. ***************************
. 
. replace voteimp_w1=.  if  voteimp_w1 ==99
(76 real changes made, 76 to missing)

. replace voteimp_w2=.  if  voteimp_w2 ==99
(59 real changes made, 59 to missing)

. replace voteimp_w3=.  if  voteimp_w3 ==99
(53 real changes made, 53 to missing)

. 
. gen d_voteimp_may=voteimp_w2-voteimp_w1
(396 missing values generated)

. gen d_voteimp_nov=voteimp_w3-voteimp_w1
(794 missing values generated)

. 
. replace voteduty_w1=. if voteduty_w1 ==99
(55 real changes made, 55 to missing)

. replace voteduty_w2=. if voteduty_w2 ==99
(38 real changes made, 38 to missing)

. replace voteduty_w3=. if voteduty_w3 ==99
(42 real changes made, 42 to missing)

. 
. gen d_voteduty_may=voteduty_w2-voteduty_w1
(366 missing values generated)

. gen d_voteduty_nov=voteduty_w3-voteduty_w1
(776 missing values generated)

. 
. replace cleanelections_w1=.  if  cleanelections_w1==99
(103 real changes made, 103 to missing)

. replace cleanelections_w2=.  if  cleanelections_w2==99
(74 real changes made, 74 to missing)

. replace cleanelections_w3=.  if  cleanelections_w3==99
(54 real changes made, 54 to missing)

. 
. gen d_cleanelections_may=cleanelections_w2-cleanelections_w1
(433 missing values generated)

. gen d_cleanelections_nov=cleanelections_w3-cleanelections_w1
(811 missing values generated)

. 
. replace trustparties_w1=. if  trustparties_w1==99
(69 real changes made, 69 to missing)

. replace trustparties_w2=. if  trustparties_w2==99
(49 real changes made, 49 to missing)

. replace trustparties_w3=. if  trustparties_w3==99
(42 real changes made, 42 to missing)

. 
. gen d_trustparties_may=trustparties_w2-trustparties_w1
(389 missing values generated)

. gen d_trustparties_nov=trustparties_w3-trustparties_w1
(790 missing values generated)

. 
. replace  freedomideas_w1=. if freedomideas_w1==99
(69 real changes made, 69 to missing)

. replace  freedomideas_w2=. if freedomideas_w2==99
(53 real changes made, 53 to missing)

. replace  freedomideas_w3=. if freedomideas_w3==99
(51 real changes made, 51 to missing)

. 
. gen d_freedomideas_may=freedomideas_w2-freedomideas_w1
(388 missing values generated)

. gen d_freedomideas_nov=freedomideas_w3-freedomideas_w1
(790 missing values generated)

. 
. replace citizensimp_w1=. if citizensimp_w1==99
(54 real changes made, 54 to missing)

. replace citizensimp_w2=. if citizensimp_w2==99
(46 real changes made, 46 to missing)

. replace citizensimp_w3=. if citizensimp_w3==99
(38 real changes made, 38 to missing)

. 
. gen d_citizensimp_may=citizensimp_w2-citizensimp_w1
(370 missing values generated)

. gen d_citizensimp_nov=citizensimp_w3-citizensimp_w1
(770 missing values generated)

. 
. replace votenotimp_w1=. if votenotimp_w1==99
(54 real changes made, 54 to missing)

. replace votenotimp_w2=. if votenotimp_w2==99
(46 real changes made, 46 to missing)

. replace votenotimp_w3=. if votenotimp_w3==99
(44 real changes made, 44 to missing)

. 
. gen d_votenotimp_may=votenotimp_w2-votenotimp_w1
(370 missing values generated)

. gen d_votenotimp_nov=votenotimp_w3-votenotimp_w1
(774 missing values generated)

. 
. replace corruption_w1=. if   corruption_w1==99
(62 real changes made, 62 to missing)

. replace corruption_w2=. if    corruption_w2==99
(47 real changes made, 47 to missing)

. replace corruption_w3=. if    corruption_w3==99
(42 real changes made, 42 to missing)

. 
. gen d_corruption_may= corruption_w2-corruption_w1
(378 missing values generated)

. gen d_corruption_nov= corruption_w3-corruption_w1
(777 missing values generated)

. 
. replace dontunderstandpolitics_w1=. if   dontunderstandpolitics_w1==99
(75 real changes made, 75 to missing)

. replace dontunderstandpolitics_w2=. if    dontunderstandpolitics_w2==99
(54 real changes made, 54 to missing)

. replace dontunderstandpolitics_w3=. if    dontunderstandpolitics_w3==99
(46 real changes made, 46 to missing)

. 
. gen d_dontunderstandpolitics_may=dontunderstandpolitics_w2-dontunderstandpolitics_w1
(390 missing values generated)

. gen d_dontunderstandpolitics_nov=dontunderstandpolitics_w3-dontunderstandpolitics_w1
(791 missing values generated)

. 
. replace demonotworking_w1=. if   demonotworking_w1==99
(68 real changes made, 68 to missing)

. replace demonotworking_w2=. if    demonotworking_w2==99
(53 real changes made, 53 to missing)

. replace demonotworking_w3=. if    demonotworking_w3==99
(37 real changes made, 37 to missing)

. 
. gen d_demonotworking_may=demonotworking_w2-demonotworking_w1
(393 missing values generated)

. gen d_demonotworking_nov=demonotworking_w3-demonotworking_w1
(783 missing values generated)

. 
. replace systnotworried_w1=. if    systnotworried_w1==99
(71 real changes made, 71 to missing)

. replace systnotworried_w2=. if    systnotworried_w2==99
(56 real changes made, 56 to missing)

. replace systnotworried_w3=. if    systnotworried_w3==99
(46 real changes made, 46 to missing)

. 
. gen d_systnotworried_may=systnotworried_w2-systnotworried_w1
(391 missing values generated)

. gen d_systnotworried_nov=systnotworried_w3-systnotworried_w1
(786 missing values generated)

. 
. 
. 
. reg d_voteimp_may  voteimp_w1 vote_2016 treatment  female age low_education high_educat i.employ
> mentstatus 

      Source |       SS           df       MS      Number of obs   =     1,524
-------------+----------------------------------   F(11, 1512)     =     60.36
       Model |  3857.76106        11  350.705551   Prob > F        =    0.0000
    Residual |  8784.90758     1,512  5.81012406   R-squared       =    0.3051
-------------+----------------------------------   Adj R-squared   =    0.3001
       Total |  12642.6686     1,523  8.30116128   Root MSE        =    2.4104

----------------------------------------------------------------------------------
   d_voteimp_may | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
      voteimp_w1 |   -.531968    .021424   -24.83   0.000    -.5739919   -.4899441
       vote_2016 |   .3336752   .1844604     1.81   0.071      -.02815    .6955005
       treatment |  -.2049011   .1646877    -1.24   0.214    -.5279416    .1181394
          female |  -.3519806    .132315    -2.66   0.008    -.6115211   -.0924401
             age |   .0165544   .0063107     2.62   0.009     .0041757    .0289332
   low_education |  -.8187062   .2326407    -3.52   0.000    -1.275039   -.3623735
  high_education |   .1570007    .135797     1.16   0.248    -.1093698    .4233712
                 |
employmentstatus |
        Retired  |   .1318824   .2613606     0.50   0.614    -.3807853    .6445502
     Unemployed  |  -.0788787   .1917059    -0.41   0.681    -.4549163     .297159
        Student  |   .5050284   .2431277     2.08   0.038     .0281251    .9819316
    Housechores  |   .4307153   .4213882     1.02   0.307    -.3958521    1.257283
                 |
           _cons |    2.72949   .3357501     8.13   0.000     2.070905    3.388076
----------------------------------------------------------------------------------

. estimates store A

. reg  d_citizensimp_may citizensimp_w1 vote_2016 treatment  female age low_education high_educat 
> i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,544
-------------+----------------------------------   F(11, 1532)     =     70.03
       Model |  5735.93526        11   521.44866   Prob > F        =    0.0000
    Residual |  11407.6476     1,532  7.44624519   R-squared       =    0.3346
-------------+----------------------------------   Adj R-squared   =    0.3298
       Total |  17143.5829     1,543  11.1105528   Root MSE        =    2.7288

----------------------------------------------------------------------------------
d_citizensimp_~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
  citizensimp_w1 |  -.5808883   .0211275   -27.49   0.000    -.6223301   -.5394464
       vote_2016 |   .0385964   .2024487     0.19   0.849    -.3585096    .4357023
       treatment |   .0581761   .1842581     0.32   0.752    -.3032487    .4196008
          female |  -.2862502   .1482499    -1.93   0.054    -.5770444     .004544
             age |   .0166858    .007058     2.36   0.018     .0028414    .0305302
   low_education |  -.1607382   .2603999    -0.62   0.537    -.6715162    .3500398
  high_education |  -.0237634   .1527552    -0.16   0.876    -.3233949    .2758681
                 |
employmentstatus |
        Retired  |   .1000922   .2948032     0.34   0.734    -.4781684    .6783528
     Unemployed  |  -.4699617   .2155345    -2.18   0.029    -.8927356   -.0471878
        Student  |   .5678333   .2733357     2.08   0.038     .0316816    1.103985
    Housechores  |  -.1259157   .4816149    -0.26   0.794     -1.07061    .8187784
                 |
           _cons |   2.685702   .3739633     7.18   0.000     1.952168    3.419237
----------------------------------------------------------------------------------

. estimates store B

. reg  d_cleanelections_may  cleanelections_w1 vote_2016 treatment  female age low_education high_
> educat i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,503
-------------+----------------------------------   F(11, 1491)     =     49.42
       Model |  2613.67822        11  237.607111   Prob > F        =    0.0000
    Residual |  7168.13948     1,491  4.80760528   R-squared       =    0.2672
-------------+----------------------------------   Adj R-squared   =    0.2618
       Total |   9781.8177     1,502  6.51252843   Root MSE        =    2.1926

-----------------------------------------------------------------------------------
d_cleanelection~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
cleanelections_w1 |  -.4330444   .0188976   -22.92   0.000    -.4701131   -.3959758
        vote_2016 |   .4213729   .1668341     2.53   0.012     .0941184    .7486275
        treatment |   .2609505   .1504658     1.73   0.083    -.0341967    .5560978
           female |   .4066307   .1205472     3.37   0.001     .1701705    .6430909
              age |   .0266924   .0058898     4.53   0.000     .0151392    .0382455
    low_education |  -.4722694   .2163611    -2.18   0.029    -.8966738   -.0478649
   high_education |   .2728693   .1241416     2.20   0.028     .0293585    .5163801
                  |
 employmentstatus |
         Retired  |   -.141941   .2389177    -0.59   0.553    -.6105916    .3267095
      Unemployed  |   -.073124   .1774795    -0.41   0.680    -.4212601    .2750121
         Student  |   .5865927   .2245078     2.61   0.009      .146208    1.026977
     Housechores  |   .1481218   .3928551     0.38   0.706    -.6224855    .9187292
                  |
            _cons |   1.028818   .3025005     3.40   0.001     .4354459     1.62219
-----------------------------------------------------------------------------------

. estimates store C

. reg  d_trustparties_may trustparties_w1 vote_2016 treatment  female age low_education high_educa
> t i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,529
-------------+----------------------------------   F(11, 1517)     =     54.37
       Model |  2537.51996        11  230.683633   Prob > F        =    0.0000
    Residual |  6436.76519     1,517  4.24308846   R-squared       =    0.2828
-------------+----------------------------------   Adj R-squared   =    0.2776
       Total |  8974.28515     1,528  5.87322327   Root MSE        =    2.0599

----------------------------------------------------------------------------------
d_trustparties~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 trustparties_w1 |  -.5004523   .0207925   -24.07   0.000    -.5412374   -.4596672
       vote_2016 |   .6873334   .1539591     4.46   0.000     .3853382    .9893286
       treatment |  -.0152941   .1400853    -0.11   0.913    -.2900755    .2594874
          female |  -.0445091   .1124881    -0.40   0.692    -.2651579    .1761397
             age |   .0035541   .0053815     0.66   0.509    -.0070019      .01411
   low_education |  -.5392276    .198845    -2.71   0.007    -.9292678   -.1491875
  high_education |  -.0141152   .1159225    -0.12   0.903    -.2415004    .2132701
                 |
employmentstatus |
        Retired  |  -.0455572   .2241554    -0.20   0.839    -.4852445      .39413
     Unemployed  |  -.0140857   .1641776    -0.09   0.932    -.3361248    .3079534
        Student  |   .3306981   .2067988     1.60   0.110    -.0749438    .7363399
    Housechores  |    .536381   .3684304     1.46   0.146    -.1863059    1.259068
                 |
           _cons |   1.050157   .2777862     3.78   0.000     .5052711    1.595043
----------------------------------------------------------------------------------

. estimates store D

. reg   d_votenotimp_may  votenotimp_w1 vote_2016 treatment  female age low_education high_educat 
> i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,542
-------------+----------------------------------   F(11, 1530)     =     54.25
       Model |  4568.61273        11   415.32843   Prob > F        =    0.0000
    Residual |  11713.2193     1,530  7.65569889   R-squared       =    0.2806
-------------+----------------------------------   Adj R-squared   =    0.2754
       Total |   16281.832     1,541  10.5657573   Root MSE        =    2.7669

----------------------------------------------------------------------------------
d_votenotimp_may | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
   votenotimp_w1 |  -.5389811   .0226055   -23.84   0.000     -.583322   -.4946401
       vote_2016 |  -.3457309   .2084757    -1.66   0.097    -.7546593    .0631974
       treatment |   .1653428   .1879604     0.88   0.379    -.2033444    .5340301
          female |   .0108764   .1504755     0.07   0.942    -.2842836    .3060364
             age |   .0107721   .0071723     1.50   0.133    -.0032965    .0248407
   low_education |   .1507183   .2656541     0.57   0.571    -.3703664     .671803
  high_education |  -.2487794   .1552332    -1.60   0.109    -.5532718     .055713
                 |
employmentstatus |
        Retired  |  -.4709468   .2998197    -1.57   0.116    -1.059048    .1171543
     Unemployed  |   .2951074   .2188236     1.35   0.178    -.1341186    .7243334
        Student  |  -.1487746   .2773939    -0.54   0.592     -.692887    .3953379
    Housechores  |  -.4018932    .483984    -0.83   0.406    -1.351235    .5474489
                 |
           _cons |   1.827271   .3894505     4.69   0.000     1.063358    2.591185
----------------------------------------------------------------------------------

. estimates store E

. reg  d_voteduty_may  voteduty_w1 vote_2016 treatment  female age low_education high_educat i.emp
> loymentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,548
-------------+----------------------------------   F(11, 1536)     =     51.24
       Model |  2616.73351        11  237.884865   Prob > F        =    0.0000
    Residual |  7131.24065     1,536   4.6427348   R-squared       =    0.2684
-------------+----------------------------------   Adj R-squared   =    0.2632
       Total |  9747.97416     1,547  6.30121148   Root MSE        =    2.1547

----------------------------------------------------------------------------------
  d_voteduty_may | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
     voteduty_w1 |  -.4878697    .021231   -22.98   0.000    -.5295145    -.446225
       vote_2016 |   .3337297   .1646437     2.03   0.043     .0107794    .6566799
       treatment |  -.2525798   .1453574    -1.74   0.082    -.5376999    .0325402
          female |  -.0827627   .1177104    -0.70   0.482    -.3136528    .1481273
             age |   .0074353   .0055873     1.33   0.183    -.0035242    .0183948
   low_education |    .169414   .2068376     0.82   0.413    -.2362999    .5751279
  high_education |  -.1170596   .1204941    -0.97   0.331      -.35341    .1192908
                 |
employmentstatus |
        Retired  |  -.3459811   .2318928    -1.49   0.136    -.8008411    .1088789
     Unemployed  |  -.2123596   .1703685    -1.25   0.213     -.546539    .1218199
        Student  |    .076287    .214536     0.36   0.722    -.3445276    .4971015
    Housechores  |  -.0923763   .3706253    -0.25   0.803    -.8193614    .6346089
                 |
           _cons |   3.624616   .3198326    11.33   0.000     2.997262    4.251971
----------------------------------------------------------------------------------

. estimates store F

. coefplot  A, bylabel(Voting is important)  subtitle(, size(small)) || B , bylabel(Citizens are i
> mportant in Spanish politics) || C , bylabel(Elections are not fraudulent) /// 
> || E, bylabel(Voting does not change anything)  ||  D, bylabel(Trust in political parties)  ||  
> F,  bylabel(Voting is a duty) || ///
> , keep( treatment) xline(0) ylabe(1 "ATE") level(95)

. 
. 
. reg   d_freedomideas_may   freedomideas_w1 vote_2016 treatment  female age low_education high_ed
> ucat i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,532
-------------+----------------------------------   F(11, 1520)     =     56.50
       Model |  3786.12112        11  344.192829   Prob > F        =    0.0000
    Residual |  9259.81296     1,520  6.09198221   R-squared       =    0.2902
-------------+----------------------------------   Adj R-squared   =    0.2851
       Total |  13045.9341     1,531  8.52118489   Root MSE        =    2.4682

----------------------------------------------------------------------------------
d_freedomideas~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 freedomideas_w1 |  -.5773982   .0235132   -24.56   0.000      -.62352   -.5312764
       vote_2016 |   .1408558   .1825726     0.77   0.441    -.2172651    .4989767
       treatment |  -.0748227   .1680484    -0.45   0.656    -.4044541    .2548087
          female |  -.1854259   .1345968    -1.38   0.169    -.4494411    .0785893
             age |   .0293061    .006458     4.54   0.000     .0166386    .0419736
   low_education |   .1130677   .2368254     0.48   0.633    -.3514714    .5776068
  high_education |  -.2531973   .1385907    -1.83   0.068    -.5250465    .0186518
                 |
employmentstatus |
        Retired  |   .3341198   .2698181     1.24   0.216    -.1951353    .8633749
     Unemployed  |  -.2653775   .1953118    -1.36   0.174    -.6484867    .1177317
        Student  |   .1251759   .2485236     0.50   0.615    -.3623095    .6126614
    Housechores  |  -.3071676   .4536914    -0.68   0.498    -1.197095    .5827598
                 |
           _cons |   3.015603   .3636647     8.29   0.000     2.302266    3.728941
----------------------------------------------------------------------------------

. estimates store G

. reg  d_corruption_may corruption_w1 vote_2016 treatment  female age low_education high_educat i.
> employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,544
-------------+----------------------------------   F(11, 1532)     =     49.76
       Model |  2765.87517        11  251.443197   Prob > F        =    0.0000
    Residual |  7741.14037     1,532  5.05296369   R-squared       =    0.2632
-------------+----------------------------------   Adj R-squared   =    0.2580
       Total |  10507.0155     1,543  6.80947216   Root MSE        =    2.2479

----------------------------------------------------------------------------------
d_corruption_may | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
   corruption_w1 |  -.5097815   .0220323   -23.14   0.000    -.5529981   -.4665649
       vote_2016 |  -.0463873   .1654758    -0.28   0.779    -.3709703    .2781957
       treatment |  -.0036629   .1526774    -0.02   0.981    -.3031417    .2958159
          female |  -.0309725   .1218344    -0.25   0.799    -.2699524    .2080075
             age |  -.0073711   .0058389    -1.26   0.207    -.0188243    .0040821
   low_education |   .1705241   .2152269     0.79   0.428    -.2516464    .5926945
  high_education |   -.116376     .12589    -0.92   0.355    -.3633109    .1305589
                 |
employmentstatus |
        Retired  |  -.3027421   .2436147    -1.24   0.214    -.7805956    .1751115
     Unemployed  |   .2317688   .1783307     1.30   0.194    -.1180294     .581567
        Student  |   -.279203   .2241857    -1.25   0.213    -.7189464    .1605403
    Housechores  |   .2947349   .4067078     0.72   0.469     -.503028    1.092498
                 |
           _cons |   3.695058   .3477975    10.62   0.000     3.012848    4.377267
----------------------------------------------------------------------------------

. estimates store H

. reg  d_dontunderstandpolitics_may dontunderstandpolitics_w1 vote_2016 treatment  female age low_
> education high_educat i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,530
-------------+----------------------------------   F(11, 1518)     =     64.05
       Model |  3870.36164        11  351.851058   Prob > F        =    0.0000
    Residual |  8339.53967     1,518   5.4937679   R-squared       =    0.3170
-------------+----------------------------------   Adj R-squared   =    0.3120
       Total |  12209.9013     1,529  7.98554696   Root MSE        =    2.3439

-------------------------------------------------------------------------------------------
d_dontunderstandpolitic~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
dontunderstandpolitics_w1 |  -.5596134   .0214347   -26.11   0.000    -.6016581   -.5175686
                vote_2016 |  -.1447292   .1744918    -0.83   0.407    -.4869998    .1975413
                treatment |   .0518896   .1590917     0.33   0.744    -.2601732    .3639524
                   female |  -.3218549   .1284087    -2.51   0.012    -.5737322   -.0699777
                      age |  -.0155915   .0061168    -2.55   0.011    -.0275897   -.0035933
            low_education |    .659052   .2265914     2.91   0.004     .2145866    1.103517
           high_education |  -.3667774   .1338659    -2.74   0.006    -.6293592   -.1041956
                          |
         employmentstatus |
                 Retired  |  -.2073026   .2551885    -0.81   0.417     -.707862    .2932568
              Unemployed  |   .1258168   .1857349     0.68   0.498    -.2385075    .4901411
                 Student  |  -.5272073   .2368453    -2.23   0.026    -.9917861   -.0626285
             Housechores  |   .0306482    .408907     0.07   0.940    -.7714343    .8327308
                          |
                    _cons |   3.339457   .3379452     9.88   0.000     2.676568    4.002346
-------------------------------------------------------------------------------------------

. estimates store I

. reg  d_demonotworking_may demonotworking_w1 vote_2016 treatment  female age low_education high_e
> ducat i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,530
-------------+----------------------------------   F(11, 1518)     =     63.03
       Model |  4898.17759        11  445.288872   Prob > F        =    0.0000
    Residual |  10724.9472     1,518  7.06518264   R-squared       =    0.3135
-------------+----------------------------------   Adj R-squared   =    0.3085
       Total |  15623.1248     1,529  10.2178711   Root MSE        =     2.658

-----------------------------------------------------------------------------------
d_demonotworkin~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
demonotworking_w1 |  -.5914471   .0226787   -26.08   0.000     -.635932   -.5469621
        vote_2016 |  -.1853814   .1984802    -0.93   0.350    -.5747058     .203943
        treatment |   .0643198   .1807446     0.36   0.722    -.2902158    .4188555
           female |   .1013474   .1448681     0.70   0.484    -.1828154    .3855103
              age |  -.0187199   .0069497    -2.69   0.007     -.032352   -.0050878
    low_education |     .30276   .2554717     1.19   0.236    -.1983549     .803875
   high_education |  -.0764282   .1497248    -0.51   0.610    -.3701174    .2172611
                  |
 employmentstatus |
         Retired  |  -.4315325   .2881753    -1.50   0.134    -.9967963    .1337314
      Unemployed  |   .0157682   .2108076     0.07   0.940    -.3977367    .4292732
         Student  |  -.4467311   .2697471    -1.66   0.098    -.9758475    .0823853
     Housechores  |  -.2337467   .4628104    -0.51   0.614    -1.141562    .6740689
                  |
            _cons |   4.462875   .4013907    11.12   0.000     3.675536    5.250214
-----------------------------------------------------------------------------------

. estimates store J

. reg  d_systnotworried_may systnotworried_w1 vote_2016 treatment  female age low_education high_e
> ducat i.employmentstatus    , 

      Source |       SS           df       MS      Number of obs   =     1,524
-------------+----------------------------------   F(11, 1512)     =     70.86
       Model |   4722.4156        11  429.310509   Prob > F        =    0.0000
    Residual |  9160.71038     1,512  6.05867089   R-squared       =    0.3402
-------------+----------------------------------   Adj R-squared   =    0.3354
       Total |   13883.126     1,523  9.11564411   Root MSE        =    2.4614

-----------------------------------------------------------------------------------
d_systnotworrie~y | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
systnotworried_w1 |  -.6473423    .023447   -27.61   0.000    -.6933343   -.6013503
        vote_2016 |  -.1060653   .1842623    -0.58   0.565     -.467502    .2553714
        treatment |    .156433   .1671816     0.94   0.350    -.1714994    .4843654
           female |   .2464616   .1347107     1.83   0.068    -.0177781    .5107013
              age |  -.0123463   .0064005    -1.93   0.054     -.024901    .0002085
    low_education |   .1516214   .2392868     0.63   0.526    -.3177479    .6209907
   high_education |  -.2459943   .1389215    -1.77   0.077    -.5184935    .0265049
                  |
 employmentstatus |
         Retired  |  -.4121155   .2678521    -1.54   0.124    -.9375166    .1132856
      Unemployed  |   .1903737   .1961474     0.97   0.332     -.194376    .5751235
         Student  |   -.705008   .2489803    -2.83   0.005    -1.193391   -.2166247
     Housechores  |   .1699182   .4288396     0.40   0.692    -.6712653    1.011102
                  |
            _cons |   4.602928   .3762587    12.23   0.000     3.864884    5.340972
-----------------------------------------------------------------------------------

. estimates store K

. coefplot  G, bylabel(All ideas should be freely expressed) subtitle(, size(small)) || H, bylabel
> (Corruption is pervasive) || I, bylabel(I don't understand most political issues) || ///
>  J, bylabel(Democracy is not working well)  || K, bylabel(Political system does not care) ||  //
> /
> , keep( treatment) xline(0) ylabe(1 "ATE") level(95)

. 
. 
. 
. 
. log close
      name:  <unnamed>
       log:  /Users/ignaciojurado/Dropbox/My Mac (MacBook-Pro.home)/Downloads/dataverse_files (1)/
> Replication.log
  log type:  text
 closed on:  10 Jul 2023, 18:08:03
--------------------------------------------------------------------------------------------------
